{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# =============================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
    "\n",
    "# TRTorch Getting Started - ResNet 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
    "\n",
    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
    "\n",
    "TRTorch is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. TRTorch is available to use with both PyTorch and LibTorch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning objectives\n",
    "\n",
    "This notebook demonstrates the steps for compiling a TorchScript module with TRTorch on a pretrained ResNet-50 network, and running it to test the speedup obtained.\n",
    "\n",
    "## Content\n",
    "1. [Requirements](#1)\n",
    "1. [ResNet-50 Overview](#2)\n",
    "1. [Creating TorchScript modules](#3)\n",
    "1. [Compiling with TRTorch](#4)\n",
    "1. [Conclusion](#5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Aug 25 06:25:19 2020       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 450.24       Driver Version: 450.24       CUDA Version: 11.0     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  Tesla P100-SXM2...  On   | 00000000:06:00.0 Off |                    0 |\r\n",
      "| N/A   37C    P0    43W / 300W |    891MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "|   1  Tesla P100-SXM2...  On   | 00000000:07:00.0 Off |                    0 |\r\n",
      "| N/A   35C    P0    34W / 300W |      2MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "|   2  Tesla P100-SXM2...  On   | 00000000:0A:00.0 Off |                    0 |\r\n",
      "| N/A   35C    P0    33W / 300W |      2MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "|   3  Tesla P100-SXM2...  On   | 00000000:0B:00.0 Off |                    0 |\r\n",
      "| N/A   33C    P0    32W / 300W |      2MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "|   4  Tesla P100-SXM2...  On   | 00000000:85:00.0 Off |                    0 |\r\n",
      "| N/A   34C    P0    33W / 300W |      2MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "|   5  Tesla P100-SXM2...  On   | 00000000:86:00.0 Off |                    0 |\r\n",
      "| N/A   31C    P0    35W / 300W |      2MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "|   6  Tesla P100-SXM2...  On   | 00000000:89:00.0 Off |                    0 |\r\n",
      "| N/A   36C    P0    33W / 300W |      2MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "|   7  Tesla P100-SXM2...  On   | 00000000:8A:00.0 Off |                    0 |\r\n",
      "| N/A   34C    P0    33W / 300W |      2MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"1\"></a>\n",
    "## 1. Requirements\n",
    "\n",
    "Follow the steps in `notebooks/README` to prepare a Docker container, within which you can run this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"2\"></a>\n",
    "## 2. ResNet-50 Overview\n",
    "\n",
    "\n",
    "PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our ResNet-50 model from there pretrained on ImageNet.\n",
    "\n",
    "### Model Description\n",
    "\n",
    "This ResNet-50 model is based on the [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) paper, which describes ResNet as “a method for detecting objects in images using a single deep neural network\". The input size is fixed to 32x32.\n",
    "\n",
    "<img src=\"https://pytorch.org/assets/images/resnet.png\" alt=\"alt\" width=\"50%\"/>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /root/.cache/torch/hub/pytorch_vision_v0.6.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "resnet50_model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet50', pretrained=True)\n",
    "resnet50_model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All pre-trained models expect input images normalized in the same way,\n",
    "i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.\n",
    "The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`\n",
    "and `std = [0.229, 0.224, 0.225]`.\n",
    "\n",
    "Here's a sample execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘./data’: File exists\n",
      "--2020-08-25 06:25:22--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\n",
      "Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 13.227.77.77, 13.227.77.154, 13.227.77.109, ...\n",
      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|13.227.77.77|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 24112 (24K) [image/jpeg]\n",
      "Saving to: ‘./data/img0.JPG’\n",
      "\n",
      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.001s  \n",
      "\n",
      "2020-08-25 06:25:22 (38.7 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
      "\n",
      "--2020-08-25 06:25:22--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 23.185.0.4, 2620:12a:8001::4, 2620:12a:8000::4\n",
      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|23.185.0.4|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 452718 (442K) [image/jpeg]\n",
      "Saving to: ‘./data/img1.JPG’\n",
      "\n",
      "./data/img1.JPG     100%[===================>] 442.11K  --.-KB/s    in 0.04s   \n",
      "\n",
      "2020-08-25 06:25:23 (10.9 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
      "\n",
      "--2020-08-25 06:25:23--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 361413 (353K) [image/jpeg]\n",
      "Saving to: ‘./data/img2.JPG’\n",
      "\n",
      "./data/img2.JPG     100%[===================>] 352.94K   774KB/s    in 0.5s    \n",
      "\n",
      "2020-08-25 06:25:30 (774 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
      "\n",
      "--2020-08-25 06:25:31--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.202.107, 104.18.201.107, 2606:4700::6812:c96b, ...\n",
      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.202.107|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 96063 (94K) [image/jpeg]\n",
      "Saving to: ‘./data/img3.JPG’\n",
      "\n",
      "./data/img3.JPG     100%[===================>]  93.81K  --.-KB/s    in 0.01s   \n",
      "\n",
      "2020-08-25 06:25:31 (7.44 MB/s) - ‘./data/img3.JPG’ saved [96063/96063]\n",
      "\n",
      "--2020-08-25 06:25:32--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.112.158\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.112.158|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 35363 (35K) [application/octet-stream]\n",
      "Saving to: ‘./data/imagenet_class_index.json’\n",
      "\n",
      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
      "\n",
      "2020-08-25 06:25:32 (482 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!mkdir ./data\n",
    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
    "\n",
    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (4.3.0)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.3.1)\n",
      "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow) (0.46)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2020.6.20)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.18.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib) (1.14.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.0.2; however, version 20.2.2 is available.\n",
      "You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install pillow matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "from torchvision import transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "for i in range(4):\n",
    "    img_path = './data/img%d.JPG'%i\n",
    "    img = Image.open(img_path)\n",
    "    preprocess = transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "])\n",
    "    input_tensor = preprocess(img)      \n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.imshow(img)\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of classes in ImageNet: 1000\n"
     ]
    }
   ],
   "source": [
    "import json \n",
    "  \n",
    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
    "    d = json.load(json_file)\n",
    "    \n",
    "print(\"Number of classes in ImageNet: {}\".format(len(d)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def rn50_preprocess():\n",
    "    preprocess = transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "    ])\n",
    "    return preprocess\n",
    "\n",
    "# decode the results into ([predicted class, description], probability)\n",
    "def predict(img_path, model):\n",
    "    img = Image.open(img_path)\n",
    "    preprocess = rn50_preprocess()\n",
    "    input_tensor = preprocess(img)\n",
    "    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
    "    \n",
    "    # move the input and model to GPU for speed if available\n",
    "    if torch.cuda.is_available():\n",
    "        input_batch = input_batch.to('cuda')\n",
    "        model.to('cuda')\n",
    "\n",
    "    with torch.no_grad():\n",
    "        output = model(input_batch)\n",
    "        # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
    "        \n",
    "    ind = torch.argmax(sm_output)\n",
    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/img0.JPG - Predicted: ['n02110185', 'Siberian_husky'], Probablility: 0.49295926094055176\n",
      "./data/img1.JPG - Predicted: ['n01820546', 'lorikeet'], Probablility: 0.6450406312942505\n",
      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9903154969215393\n",
      "./data/img3.JPG - Predicted: ['n01749939', 'green_mamba'], Probablility: 0.35704153776168823\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    img_path = './data/img%d.JPG'%i\n",
    "    img = Image.open(img_path)\n",
    "    \n",
    "    pred, prob = predict(img_path, resnet50_model)\n",
    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
    "\n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.imshow(img);\n",
    "    plt.axis('off');\n",
    "    plt.title(pred[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark utility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us define a helper function to benchmark a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "import torch.backends.cudnn as cudnn\n",
    "cudnn.benchmark = True\n",
    "\n",
    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
    "    input_data = torch.randn(input_shape)\n",
    "    input_data = input_data.to(\"cuda\")\n",
    "    if dtype=='fp16':\n",
    "        input_data = input_data.half()\n",
    "        \n",
    "    print(\"Warm up ...\")\n",
    "    with torch.no_grad():\n",
    "        for _ in range(nwarmup):\n",
    "            features = model(input_data)\n",
    "    torch.cuda.synchronize()\n",
    "    print(\"Start timing ...\")\n",
    "    timings = []\n",
    "    with torch.no_grad():\n",
    "        for i in range(1, nruns+1):\n",
    "            start_time = time.time()\n",
    "            features = model(input_data)\n",
    "            torch.cuda.synchronize()\n",
    "            end_time = time.time()\n",
    "            timings.append(end_time - start_time)\n",
    "            if i%100==0:\n",
    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
    "\n",
    "    print(\"Input shape:\", input_data.size())\n",
    "    print(\"Output features size:\", features.size())\n",
    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 100/1000, ave batch time 162.87 ms\n",
      "Iteration 200/1000, ave batch time 162.92 ms\n",
      "Iteration 300/1000, ave batch time 162.92 ms\n",
      "Iteration 400/1000, ave batch time 162.93 ms\n",
      "Iteration 500/1000, ave batch time 162.93 ms\n",
      "Iteration 600/1000, ave batch time 162.93 ms\n",
      "Iteration 700/1000, ave batch time 162.93 ms\n",
      "Iteration 800/1000, ave batch time 162.94 ms\n",
      "Iteration 900/1000, ave batch time 162.94 ms\n",
      "Iteration 1000/1000, ave batch time 162.94 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 162.94 ms\n"
     ]
    }
   ],
   "source": [
    "# Model benchmark without TRTorch/TensorRT\n",
    "model = resnet50_model.eval().to(\"cuda\")\n",
    "benchmark(model, input_shape=(128, 3, 224, 224), nruns=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"3\"></a>\n",
    "## 3. Creating TorchScript modules\n",
    "\n",
    "To compile with TRTorch, the model must first be in **TorchScript**. TorchScript is a programming language included in PyTorch which removes the Python dependency normal PyTorch models have. This conversion is done via a JIT compiler which given a PyTorch Module will generate an equivalent TorchScript Module. There are two paths that can be used to generate TorchScript: **Tracing** and **Scripting**. \n",
    "\n",
    "- Tracing follows execution of PyTorch generating ops in TorchScript corresponding to what it sees. \n",
    "- Scripting does an analysis of the Python code and generates TorchScript, this allows the resulting graph to include control flow which tracing cannot do. \n",
    "\n",
    "Tracing is more likely to compile successfully with TRTorch due to simplicity (though both systems are supported). We start with an example of the traced model in TorchScript."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracing\n",
    "\n",
    "Tracing follows the path of execution when the module is called and records what happens. This recording is what the TorchScript IR will describe. \n",
    "\n",
    "To trace an instance of the model, we can call torch.jit.trace with an example input. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = resnet50_model.eval().to(\"cuda\")\n",
    "traced_model = torch.jit.trace(model, [torch.randn((128, 3, 224, 224)).to(\"cuda\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can save this model and use it independently of Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is just an example, and not required for the purposes of this demo\n",
    "torch.jit.save(traced_model, \"resnet_50_traced.jit.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 100/1000, ave batch time 162.96 ms\n",
      "Iteration 200/1000, ave batch time 162.96 ms\n",
      "Iteration 300/1000, ave batch time 162.96 ms\n",
      "Iteration 400/1000, ave batch time 162.96 ms\n",
      "Iteration 500/1000, ave batch time 162.96 ms\n",
      "Iteration 600/1000, ave batch time 162.96 ms\n",
      "Iteration 700/1000, ave batch time 162.96 ms\n",
      "Iteration 800/1000, ave batch time 162.96 ms\n",
      "Iteration 900/1000, ave batch time 162.96 ms\n",
      "Iteration 1000/1000, ave batch time 162.96 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 162.96 ms\n"
     ]
    }
   ],
   "source": [
    "# Obtain the average time taken by a batch of input\n",
    "benchmark(traced_model, input_shape=(128, 3, 224, 224), nruns=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"4\"></a>\n",
    "## 4. Compiling with TRTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT.\n",
    "\n",
    "As mentioned earlier, we start with an example of TRTorch compilation with the traced model.\n",
    "\n",
    "Note that we show benchmarking results of two precisions: FP32 (single precision) and FP16 (half precision)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FP32 (single precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import trtorch\n",
    "\n",
    "# The compiled module will have precision as specified by \"op_precision\".\n",
    "# Here, it will have FP16 precision.\n",
    "trt_model_fp32 = trtorch.compile(traced_model, {\n",
    "    \"inputs\": [trtorch.Input((128, 3, 224, 224))],\n",
    "    \"enabled_precisions\": {torch.float32}, # Run with FP32\n",
    "    \"workspace_size\": 1 << 20\n",
    "})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 100/1000, ave batch time 117.05 ms\n",
      "Iteration 200/1000, ave batch time 117.06 ms\n",
      "Iteration 300/1000, ave batch time 117.10 ms\n",
      "Iteration 400/1000, ave batch time 117.14 ms\n",
      "Iteration 500/1000, ave batch time 117.19 ms\n",
      "Iteration 600/1000, ave batch time 117.22 ms\n",
      "Iteration 700/1000, ave batch time 117.25 ms\n",
      "Iteration 800/1000, ave batch time 117.29 ms\n",
      "Iteration 900/1000, ave batch time 117.36 ms\n",
      "Iteration 1000/1000, ave batch time 117.41 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 117.41 ms\n"
     ]
    }
   ],
   "source": [
    "# Obtain the average time taken by a batch of input\n",
    "benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FP16 (half precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import trtorch\n",
    "\n",
    "# The compiled module will have precision as specified by \"op_precision\".\n",
    "# Here, it will have FP16 precision.\n",
    "trt_model = trtorch.compile(traced_model, {\n",
    "    \"inputs\": [trtorch.Input((128, 3, 224, 224))],\n",
    "    \"enabled_precisions\": {torch.float, torch.half}, # Run with FP16\n",
    "    \"workspace_size\": 1 << 20\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 100/1000, ave batch time 62.79 ms\n",
      "Iteration 200/1000, ave batch time 62.78 ms\n",
      "Iteration 300/1000, ave batch time 62.79 ms\n",
      "Iteration 400/1000, ave batch time 62.78 ms\n",
      "Iteration 500/1000, ave batch time 62.78 ms\n",
      "Iteration 600/1000, ave batch time 62.78 ms\n",
      "Iteration 700/1000, ave batch time 62.79 ms\n",
      "Iteration 800/1000, ave batch time 62.79 ms\n",
      "Iteration 900/1000, ave batch time 59.37 ms\n",
      "Iteration 1000/1000, ave batch time 54.04 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 54.04 ms\n"
     ]
    }
   ],
   "source": [
    "# Obtain the average time taken by a batch of input\n",
    "benchmark(trt_model, input_shape=(128, 3, 224, 224), dtype='fp16', nruns=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"5\"></a>\n",
    "## 5. Conclusion\n",
    "\n",
    "In this notebook, we have walked through the complete process of compiling TorchScript models with TRTorch for ResNet-50 model and test the performance impact of the optimization. With TRTorch, we observe a speedup of **1.4X** with FP32, and **3.0X** with FP16.\n",
    "\n",
    "### What's next\n",
    "Now it's time to try TRTorch on your own model. Fill out issues at https://github.com/NVIDIA/TRTorch. Your involvement will help future development of TRTorch.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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